Part 1: veg data

## # A tibble: 322 × 4
## # Groups:   Crop_Type [34]
##    Crop_Type        Estimates                             Start_Year End_Year
##    <chr>            <chr>                                      <int>    <int>
##  1 Brussels sprouts Area harvested (acres)                      2007     2023
##  2 Brussels sprouts Area harvested (hectares)                   2007     2023
##  3 Brussels sprouts Area planted (acres)                        2007     2023
##  4 Brussels sprouts Area planted (hectares)                     2007     2023
##  5 Brussels sprouts Average yield per acre (pounds)             2007     2017
##  6 Brussels sprouts Average yield per hectare (kilograms)       2007     2017
##  7 Brussels sprouts Marketed production (metric tonnes)         2007     2023
##  8 Brussels sprouts Marketed production (tons)                  2007     2023
##  9 Brussels sprouts Total production (metric tonnes)            2007     2023
## 10 Brussels sprouts Total production (tons)                     2007     2023
## # ℹ 312 more rows
## # A tibble: 140 × 4
## # Groups:   Crop_Type [14]
##    Crop_Type Estimates                             Start_Year End_Year
##    <chr>     <chr>                                      <int>    <int>
##  1 asparagus Area harvested (acres)                      1982     2023
##  2 asparagus Area harvested (hectares)                   2002     2023
##  3 asparagus Area planted (acres)                        1940     2023
##  4 asparagus Area planted (hectares)                     2002     2023
##  5 asparagus Average yield per acre (pounds)             1940     2017
##  6 asparagus Average yield per hectare (kilograms)       2002     2017
##  7 asparagus Marketed production (metric tonnes)         2002     2023
##  8 asparagus Marketed production (tons)                  1982     2023
##  9 asparagus Total production (metric tonnes)            2002     2023
## 10 asparagus Total production (tons)                     1940     2023
## # ℹ 130 more rows

plot good quality replacement for yield data

plot ok quality replacement for yield data

plot soso quality replacement for yield data

plot mixed quality replacement for yield data

plot bad quality replacement for yield data

Part 6: patatoes

## [1] "There are 5  NA in the matrix X in FortStJoh station"
## [1] "Results for crop: Barley"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -810.77 -210.13  -45.77  216.09  876.48 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2747.565    132.603  20.720 5.49e-15 ***
## Month_1        3.671      3.271   1.122  0.27511    
## Month_2       -1.625      5.889  -0.276  0.78550    
## Month_3      -10.737      6.665  -1.611  0.12288    
## Month_4      -21.387      5.908  -3.620  0.00171 ** 
## Month_5       27.866      8.546   3.261  0.00391 ** 
## Month_6       -5.543      4.113  -1.348  0.19282    
## Month_7      -16.804      9.772  -1.720  0.10094    
## Month_8      -15.634     20.745  -0.754  0.45987    
## Month_9      -34.634     18.477  -1.874  0.07555 .  
## Month_10     -46.651     33.615  -1.388  0.18047    
## Month_11      10.350      8.267   1.252  0.22500    
## Month_12       0.548      4.713   0.116  0.90859    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 442.6 on 20 degrees of freedom
## Multiple R-squared:  0.6662, Adjusted R-squared:  0.466 
## F-statistic: 3.327 on 12 and 20 DF,  p-value: 0.008594

## [1] "Results for crop: Canola"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -495.78 -201.82  -22.08  224.12  494.01 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.640e+03  1.049e+02  15.627 1.13e-12 ***
## Month_1      8.420e-01  2.588e+00   0.325  0.74832    
## Month_2     -3.685e-01  4.660e+00  -0.079  0.93776    
## Month_3     -2.422e+00  5.274e+00  -0.459  0.65100    
## Month_4      3.546e-01  4.674e+00   0.076  0.94028    
## Month_5      2.033e+01  6.762e+00   3.007  0.00697 ** 
## Month_6      4.823e-03  3.254e+00   0.001  0.99883    
## Month_7      1.363e+01  7.732e+00   1.763  0.09316 .  
## Month_8     -3.024e+01  1.641e+01  -1.842  0.08030 .  
## Month_9     -9.163e+00  1.462e+01  -0.627  0.53789    
## Month_10     9.810e+00  2.660e+01   0.369  0.71614    
## Month_11     2.620e+00  6.541e+00   0.401  0.69294    
## Month_12     2.417e+00  3.729e+00   0.648  0.52432    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 350.2 on 20 degrees of freedom
## Multiple R-squared:  0.5118, Adjusted R-squared:  0.2189 
## F-statistic: 1.748 on 12 and 20 DF,  p-value: 0.1302

## [1] "Results for crop: Oats"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -686.30 -298.04   14.58  346.99  723.26 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2594.6436   152.9915  16.959 2.45e-13 ***
## Month_1        6.9024     3.7741   1.829  0.08236 .  
## Month_2       -6.6119     6.7945  -0.973  0.34211    
## Month_3        0.7560     7.6902   0.098  0.92267    
## Month_4      -13.0867     6.8161  -1.920  0.06924 .  
## Month_5       30.1494     9.8599   3.058  0.00621 ** 
## Month_6        2.8667     4.7455   0.604  0.55257    
## Month_7      -13.1842    11.2744  -1.169  0.25599    
## Month_8      -20.3265    23.9352  -0.849  0.40581    
## Month_9       -8.4361    21.3175  -0.396  0.69649    
## Month_10     -45.3332    38.7836  -1.169  0.25620    
## Month_11       0.5282     9.5375   0.055  0.95638    
## Month_12       6.9356     5.4377   1.275  0.21676    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 510.6 on 20 degrees of freedom
## Multiple R-squared:  0.5482, Adjusted R-squared:  0.2772 
## F-statistic: 2.023 on 12 and 20 DF,  p-value: 0.07897

## [1] "Results for crop: Peas, dry"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -587.52 -153.37    4.09  172.14  555.40 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2283.3846   106.2762  21.485 2.74e-15 ***
## Month_1        0.4053     2.6217   0.155   0.8787    
## Month_2        4.8273     4.7198   1.023   0.3186    
## Month_3        7.8679     5.3420   1.473   0.1564    
## Month_4       -9.8101     4.7348  -2.072   0.0514 .  
## Month_5        9.9292     6.8493   1.450   0.1627    
## Month_6       -2.0050     3.2965  -0.608   0.5499    
## Month_7      -18.6597     7.8318  -2.383   0.0272 *  
## Month_8      -27.2808    16.6267  -1.641   0.1165    
## Month_9      -18.9174    14.8083  -1.277   0.2161    
## Month_10      22.9643    26.9412   0.852   0.4041    
## Month_11       5.6301     6.6252   0.850   0.4055    
## Month_12      -1.0693     3.7773  -0.283   0.7800    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 354.7 on 20 degrees of freedom
## Multiple R-squared:  0.632,  Adjusted R-squared:  0.4112 
## F-statistic: 2.863 on 12 and 20 DF,  p-value: 0.01824

## [1] "Results for crop: Rye, fall remaining"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -332.65 -101.24  -16.46  128.25  327.91 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2918.466     69.585  41.941  < 2e-16 ***
## Month_1       -5.645      1.717  -3.288  0.00367 ** 
## Month_2      -11.845      3.090  -3.833  0.00104 ** 
## Month_3       -8.053      3.498  -2.302  0.03219 *  
## Month_4       -2.919      3.100  -0.942  0.35765    
## Month_5        1.866      4.485   0.416  0.68170    
## Month_6       -4.689      2.158  -2.172  0.04202 *  
## Month_7        6.813      5.128   1.329  0.19895    
## Month_8      -12.300     10.886  -1.130  0.27190    
## Month_9        2.846      9.696   0.294  0.77216    
## Month_10     -21.121     17.640  -1.197  0.24516    
## Month_11      10.945      4.338   2.523  0.02021 *  
## Month_12       1.048      2.473   0.424  0.67638    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 232.3 on 20 degrees of freedom
## Multiple R-squared:  0.8199, Adjusted R-squared:  0.7118 
## F-statistic: 7.588 on 12 and 20 DF,  p-value: 4.424e-05

## [1] "Results for crop: Wheat, spring"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -956.43 -258.19  -44.64  364.59  696.77 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2734.4157   165.1893  16.553 3.86e-13 ***
## Month_1       -0.1194     4.0750  -0.029  0.97692    
## Month_2       -5.4679     7.3362  -0.745  0.46474    
## Month_3        6.0417     8.3033   0.728  0.47528    
## Month_4      -22.9612     7.3595  -3.120  0.00540 ** 
## Month_5       30.8880    10.6461   2.901  0.00883 ** 
## Month_6       -1.6546     5.1238  -0.323  0.75011    
## Month_7      -29.6905    12.1732  -2.439  0.02417 *  
## Month_8       -5.4537    25.8436  -0.211  0.83500    
## Month_9       12.9619    23.0171   0.563  0.57960    
## Month_10     -42.4457    41.8757  -1.014  0.32287    
## Month_11     -12.2600    10.2979  -1.191  0.24777    
## Month_12       3.6215     5.8713   0.617  0.54431    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 551.4 on 20 degrees of freedom
## Multiple R-squared:  0.6203, Adjusted R-squared:  0.3925 
## F-statistic: 2.723 on 12 and 20 DF,  p-value: 0.02309

Part 2: patato data

## [1] "There are 7  NA in the matrix X in Kelowna station"

## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -74.173 -28.423  -2.651  29.108  75.982 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 253.3923     6.4844  39.077   <2e-16 ***
## Month_1      -1.0241     0.7347  -1.394   0.1696    
## Month_2      -2.0828     0.8270  -2.518   0.0151 *  
## Month_3       0.4237     0.6457   0.656   0.5147    
## Month_4       0.3979     0.7355   0.541   0.5910    
## Month_5       0.8744     0.4300   2.033   0.0474 *  
## Month_6       0.3296     0.4143   0.796   0.4300    
## Month_7       0.6135     0.7436   0.825   0.4133    
## Month_8       3.4547     2.1860   1.580   0.1205    
## Month_9       2.2764     2.2905   0.994   0.3252    
## Month_10      0.8146     2.4062   0.339   0.7364    
## Month_11      0.8313     1.0375   0.801   0.4268    
## Month_12      0.2330     0.7185   0.324   0.7472    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39.01 on 49 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3678, Adjusted R-squared:  0.213 
## F-statistic: 2.376 on 12 and 49 DF,  p-value: 0.0167
## [1] "There are 6  NA in the matrix X in Abbotsford station"

## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -76.591 -25.158   2.764  20.422  78.130 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 238.0161     6.2802  37.900   <2e-16 ***
## Month_1       0.2120     0.6940   0.306   0.7612    
## Month_2      -0.9618     0.7972  -1.206   0.2333    
## Month_3       1.1482     0.6190   1.855   0.0695 .  
## Month_4       0.2776     0.4761   0.583   0.5625    
## Month_5       0.6459     0.3066   2.106   0.0402 *  
## Month_6       0.5502     0.2503   2.198   0.0326 *  
## Month_7       0.5819     0.3767   1.545   0.1287    
## Month_8       1.4800     0.8984   1.647   0.1058    
## Month_9       2.1554     1.2924   1.668   0.1016    
## Month_10     -2.0377     2.2403  -0.910   0.3674    
## Month_11     -0.1693     1.5896  -0.107   0.9156    
## Month_12      0.6542     0.8210   0.797   0.4293    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37.57 on 50 degrees of freedom
## Multiple R-squared:  0.4017, Adjusted R-squared:  0.2581 
## F-statistic: 2.798 on 12 and 50 DF,  p-value: 0.00537
## [1] "There are 7  NA in the matrix X in Kelowna station"

## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -42.414 -10.065  -0.746  10.162  41.305 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.15763    2.97528  -0.725    0.472
## Month_1     -0.21288    0.33713  -0.631    0.531
## Month_2     -0.14369    0.37948  -0.379    0.707
## Month_3     -0.10422    0.29626  -0.352    0.726
## Month_4      0.25659    0.33749   0.760    0.451
## Month_5      0.05314    0.19732   0.269    0.789
## Month_6      0.01764    0.19008   0.093    0.926
## Month_7      0.22220    0.34117   0.651    0.518
## Month_8     -1.37895    1.00303  -1.375    0.175
## Month_9     -0.70133    1.05095  -0.667    0.508
## Month_10     0.24395    1.10405   0.221    0.826
## Month_11     0.31184    0.47602   0.655    0.515
## Month_12     0.29870    0.32969   0.906    0.369
## 
## Residual standard error: 17.9 on 49 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1383, Adjusted R-squared:  -0.07268 
## F-statistic: 0.6556 on 12 and 49 DF,  p-value: 0.784
## [1] "There are 7  NA in the matrix X in Kelowna station"

## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -44.448  -7.620  -0.728   8.497  40.598 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.95467    2.99008  -0.654    0.516
## Month_1     -0.15930    0.33505  -0.475    0.637
## Month_2     -0.04336    0.36974  -0.117    0.907
## Month_3      0.02127    0.30023   0.071    0.944
## Month_4      0.12031    0.34145   0.352    0.726
## Month_5     -0.08646    0.20025  -0.432    0.668
## Month_6      0.04892    0.18545   0.264    0.793
## Month_7      0.16642    0.34576   0.481    0.633
## Month_8     -1.33634    1.02896  -1.299    0.200
## Month_9     -0.69091    1.04250  -0.663    0.511
## Month_10     0.05866    1.08950   0.054    0.957
## Month_11     0.18527    0.46411   0.399    0.692
## Month_12     0.23704    0.32482   0.730    0.469
## 
## Residual standard error: 17.35 on 47 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.09791,    Adjusted R-squared:  -0.1324 
## F-statistic: 0.4251 on 12 and 47 DF,  p-value: 0.9456
## [1] "There are 7 NA in the matrix X in Kelowna station"

## [[1]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -66.805 -32.650  -4.853  31.271 107.397 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 256.3213     5.5035  46.574   <2e-16 ***
## Predictor    -1.2670     0.6753  -1.876   0.0654 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.76 on 61 degrees of freedom
## Multiple R-squared:  0.05456,    Adjusted R-squared:  0.03907 
## F-statistic: 3.521 on 1 and 61 DF,  p-value: 0.0654
## 
## 
## [[2]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -81.970 -31.565  -5.315  34.240 106.594 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 252.4300     5.6819  44.427  < 2e-16 ***
## Predictor    -1.9819     0.7303  -2.714  0.00864 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 41.54 on 61 degrees of freedom
## Multiple R-squared:  0.1077, Adjusted R-squared:  0.09309 
## F-statistic: 7.364 on 1 and 61 DF,  p-value: 0.008637
## 
## 
## [[3]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -75.647 -32.060   1.217  31.758 111.414 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 258.3407     5.5563  46.495   <2e-16 ***
## Predictor     0.1299     0.6231   0.208    0.836    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43.96 on 61 degrees of freedom
## Multiple R-squared:  0.0007119,  Adjusted R-squared:  -0.01567 
## F-statistic: 0.04346 on 1 and 61 DF,  p-value: 0.8356
## 
## 
## [[4]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -78.125 -33.301   4.284  30.903  98.031 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 259.4956     5.5654  46.627   <2e-16 ***
## Predictor     0.8320     0.7418   1.122    0.266    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43.53 on 61 degrees of freedom
## Multiple R-squared:  0.02021,    Adjusted R-squared:  0.004144 
## F-statistic: 1.258 on 1 and 61 DF,  p-value: 0.2664
## 
## 
## [[5]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -75.396 -35.001   1.673  35.634  83.519 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 255.8012     5.4512  46.925   <2e-16 ***
## Predictor     0.9700     0.4313   2.249   0.0281 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.26 on 61 degrees of freedom
## Multiple R-squared:  0.07657,    Adjusted R-squared:  0.06143 
## F-statistic: 5.058 on 1 and 61 DF,  p-value: 0.02813
## 
## 
## [[6]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -83.556 -29.442   0.235  27.432 108.720 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 258.2837     5.3297  48.462   <2e-16 ***
## Predictor     0.7456     0.3357   2.221   0.0301 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.3 on 61 degrees of freedom
## Multiple R-squared:  0.0748, Adjusted R-squared:  0.05963 
## F-statistic: 4.932 on 1 and 61 DF,  p-value: 0.03009
## 
## 
## [[7]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -93.47 -29.47   4.48  31.00 110.20 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 257.4176     5.4499  47.233   <2e-16 ***
## Predictor     1.0818     0.6433   1.681   0.0978 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.99 on 61 degrees of freedom
## Multiple R-squared:  0.0443, Adjusted R-squared:  0.02863 
## F-statistic: 2.827 on 1 and 61 DF,  p-value: 0.09778
## 
## 
## [[8]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -82.046 -32.733   5.984  26.482  90.138 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  261.533      5.610  46.620   <2e-16 ***
## Predictor      4.142      2.137   1.939   0.0572 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.68 on 61 degrees of freedom
## Multiple R-squared:  0.05804,    Adjusted R-squared:  0.0426 
## F-statistic: 3.759 on 1 and 61 DF,  p-value: 0.05716
## 
## 
## [[9]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -78.43 -32.16   2.58  31.56 110.58 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  259.492      5.901  43.973   <2e-16 ***
## Predictor      1.219      2.375   0.513     0.61    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43.88 on 61 degrees of freedom
## Multiple R-squared:  0.004298,   Adjusted R-squared:  -0.01202 
## F-statistic: 0.2633 on 1 and 61 DF,  p-value: 0.6097
## 
## 
## [[10]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -75.271 -31.428   0.683  29.028 113.409 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  255.326      6.155  41.484   <2e-16 ***
## Predictor     -2.654      2.384  -1.113     0.27    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43.54 on 61 degrees of freedom
## Multiple R-squared:  0.01991,    Adjusted R-squared:  0.003839 
## F-statistic: 1.239 on 1 and 61 DF,  p-value: 0.27
## 
## 
## [[11]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -75.894 -33.908   4.637  32.147 113.396 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 258.8379     5.5353  46.761   <2e-16 ***
## Predictor     1.4081     0.9497   1.483    0.143    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43.55 on 60 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.03534,    Adjusted R-squared:  0.01926 
## F-statistic: 2.198 on 1 and 60 DF,  p-value: 0.1434
## 
## 
## [[12]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -74.048 -32.027   1.627  31.474 110.242 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 258.4799     5.5688  46.416   <2e-16 ***
## Predictor     0.0652     0.7908   0.082    0.935    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43.97 on 61 degrees of freedom
## Multiple R-squared:  0.0001114,  Adjusted R-squared:  -0.01628 
## F-statistic: 0.006797 on 1 and 61 DF,  p-value: 0.9346
## [1] "There are 7 NA in the matrix X in Kelowna station"

## [[1]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.225  -7.144  -0.725   8.355  43.760 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.3438     2.1256  -0.162    0.872
## Predictor    -0.1347     0.2604  -0.517    0.607
## 
## Residual standard error: 16.32 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.004518,   Adjusted R-squared:  -0.01235 
## F-statistic: 0.2678 on 1 and 59 DF,  p-value: 0.6067
## 
## 
## [[2]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -52.267  -6.634   0.202   7.564  45.013 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.7947     2.2509  -0.353    0.725
## Predictor    -0.2197     0.2866  -0.767    0.446
## 
## Residual standard error: 16.28 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.009863,   Adjusted R-squared:  -0.006919 
## F-statistic: 0.5877 on 1 and 59 DF,  p-value: 0.4464
## 
## 
## [[3]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.053  -7.076  -0.468   7.661  43.762 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06525    2.09766  -0.031    0.975
## Predictor   -0.10831    0.23333  -0.464    0.644
## 
## Residual standard error: 16.33 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.003639,   Adjusted R-squared:  -0.01325 
## F-statistic: 0.2155 on 1 and 59 DF,  p-value: 0.6442
## 
## 
## [[4]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -52.165  -6.767  -0.976   8.876  44.489 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01599    2.13357   0.007    0.994
## Predictor    0.11011    0.28919   0.381    0.705
## 
## Residual standard error: 16.34 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.002451,   Adjusted R-squared:  -0.01446 
## F-statistic: 0.145 on 1 and 59 DF,  p-value: 0.7048
## 
## 
## [[5]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.920  -6.577  -1.282   8.549  43.534 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.03528    2.13393   0.017    0.987
## Predictor   -0.07384    0.17494  -0.422    0.675
## 
## Residual standard error: 16.34 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.00301,    Adjusted R-squared:  -0.01389 
## F-statistic: 0.1781 on 1 and 59 DF,  p-value: 0.6745
## 
## 
## [[6]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.145  -6.495  -0.900   8.014  44.608 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.16879    2.08624  -0.081    0.936
## Predictor    0.09148    0.12955   0.706    0.483
## 
## Residual standard error: 16.29 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.008381,   Adjusted R-squared:  -0.008426 
## F-statistic: 0.4987 on 1 and 59 DF,  p-value: 0.4829
## 
## 
## [[7]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.690  -7.461  -0.860   8.699  44.493 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.2249     2.0989  -0.107    0.915
## Predictor     0.1123     0.2489   0.451    0.653
## 
## Residual standard error: 16.33 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.00344,    Adjusted R-squared:  -0.01345 
## F-statistic: 0.2037 on 1 and 59 DF,  p-value: 0.6534
## 
## 
## [[8]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -46.387  -7.871  -0.618   8.411  40.488 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -1.2774     2.1858  -0.584    0.561
## Predictor    -1.2829     0.8431  -1.522    0.133
## 
## Residual standard error: 16.05 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.03776,    Adjusted R-squared:  0.02145 
## F-statistic: 2.315 on 1 and 59 DF,  p-value: 0.1334
## 
## 
## [[9]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.851  -7.678   0.699   7.647  44.001 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -1.2263     2.2283  -0.550    0.584
## Predictor    -1.1594     0.8948  -1.296    0.200
## 
## Residual standard error: 16.13 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.02766,    Adjusted R-squared:  0.01118 
## F-statistic: 1.679 on 1 and 59 DF,  p-value: 0.2002
## 
## 
## [[10]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.833  -6.907  -1.584   8.155  44.254 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04718    2.35924  -0.020    0.984
## Predictor    0.07959    0.89926   0.089    0.930
## 
## Residual standard error: 16.36 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.0001328,  Adjusted R-squared:  -0.01681 
## F-statistic: 0.007834 on 1 and 59 DF,  p-value: 0.9298
## 
## 
## [[11]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -53.555  -6.350  -1.233   8.015  43.287 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.2708     2.1088  -0.128    0.898
## Predictor     0.3270     0.3591   0.910    0.366
## 
## Residual standard error: 16.33 on 58 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.01409,    Adjusted R-squared:  -0.00291 
## F-statistic: 0.8288 on 1 and 58 DF,  p-value: 0.3664
## 
## 
## [[12]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.438  -6.220  -0.707   8.187  44.789 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.05742    2.08668   0.028    0.978
## Predictor    0.29648    0.29543   1.004    0.320
## 
## Residual standard error: 16.22 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.01678,    Adjusted R-squared:  0.000119 
## F-statistic: 1.007 on 1 and 59 DF,  p-value: 0.3197
## [1] "There are 7 NA in the matrix X in Kelowna station"

## [[1]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -48.992  -9.255  -1.606  11.268  43.006 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.4578     2.2363  -0.205    0.838
## Predictor    -0.2100     0.2744  -0.765    0.447
## 
## Residual standard error: 17.38 on 61 degrees of freedom
## Multiple R-squared:  0.009509,   Adjusted R-squared:  -0.006729 
## F-statistic: 0.5856 on 1 and 61 DF,  p-value: 0.4471
## 
## 
## [[2]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -52.275 -10.258  -0.622  10.410  45.131 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -1.2857     2.3564  -0.546    0.587
## Predictor    -0.3889     0.3029  -1.284    0.204
## 
## Residual standard error: 17.23 on 61 degrees of freedom
## Multiple R-squared:  0.02631,    Adjusted R-squared:  0.01035 
## F-statistic: 1.648 on 1 and 61 DF,  p-value: 0.204
## 
## 
## [[3]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -47.713  -9.686  -0.877  10.913  42.753 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.05739    2.19069   0.026    0.979
## Predictor   -0.23172    0.24567  -0.943    0.349
## 
## Residual standard error: 17.33 on 61 degrees of freedom
## Multiple R-squared:  0.01438,    Adjusted R-squared:  -0.001783 
## F-statistic: 0.8897 on 1 and 61 DF,  p-value: 0.3493
## 
## 
## [[4]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -52.210  -9.899  -0.031  12.005  44.366 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.2008     2.2200   0.090    0.928
## Predictor     0.2417     0.2959   0.817    0.417
## 
## Residual standard error: 17.36 on 61 degrees of freedom
## Multiple R-squared:  0.01082,    Adjusted R-squared:  -0.005394 
## F-statistic: 0.6674 on 1 and 61 DF,  p-value: 0.4171
## 
## 
## [[5]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.522  -9.357  -1.115  11.263  43.954 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.23107    2.25086  -0.103    0.919
## Predictor    0.04543    0.17809   0.255    0.799
## 
## Residual standard error: 17.45 on 61 degrees of freedom
## Multiple R-squared:  0.001066,   Adjusted R-squared:  -0.01531 
## F-statistic: 0.06508 on 1 and 61 DF,  p-value: 0.7995
## 
## 
## [[6]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.773 -11.461  -1.726  12.033  44.086 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.1287     2.1895  -0.059    0.953
## Predictor     0.1040     0.1379   0.754    0.454
## 
## Residual standard error: 17.38 on 61 degrees of freedom
## Multiple R-squared:  0.009234,   Adjusted R-squared:  -0.007008 
## F-statistic: 0.5685 on 1 and 61 DF,  p-value: 0.4537
## 
## 
## [[7]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.394  -9.848  -1.025  11.930  43.917 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.2071     2.2102  -0.094    0.926
## Predictor     0.1058     0.2609   0.405    0.687
## 
## Residual standard error: 17.43 on 61 degrees of freedom
## Multiple R-squared:  0.002688,   Adjusted R-squared:  -0.01366 
## F-statistic: 0.1644 on 1 and 61 DF,  p-value: 0.6866
## 
## 
## [[8]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -45.680  -9.609  -2.472  12.374  39.303 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -1.1762     2.2439  -0.524   0.6021  
## Predictor    -1.4279     0.8546  -1.671   0.0999 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.07 on 61 degrees of freedom
## Multiple R-squared:  0.04376,    Adjusted R-squared:  0.02809 
## F-statistic: 2.792 on 1 and 61 DF,  p-value: 0.09988
## 
## 
## [[9]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.396 -10.343   0.704  10.899  43.290 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -1.3835     2.3008  -0.601    0.550
## Predictor    -1.4685     0.9262  -1.586    0.118
## 
## Residual standard error: 17.11 on 61 degrees of freedom
## Multiple R-squared:  0.03959,    Adjusted R-squared:  0.02384 
## F-statistic: 2.514 on 1 and 61 DF,  p-value: 0.118
## 
## 
## [[10]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.598  -9.754  -1.585  11.719  43.459 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.19548    2.46798  -0.079    0.937
## Predictor   -0.07488    0.95599  -0.078    0.938
## 
## Residual standard error: 17.46 on 61 degrees of freedom
## Multiple R-squared:  0.0001006,  Adjusted R-squared:  -0.01629 
## F-statistic: 0.006135 on 1 and 61 DF,  p-value: 0.9378
## 
## 
## [[11]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -53.940 -12.010  -0.993  11.476  42.381 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.3056     2.1879  -0.140    0.889
## Predictor     0.4591     0.3754   1.223    0.226
## 
## Residual standard error: 17.21 on 60 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.02432,    Adjusted R-squared:  0.008063 
## F-statistic: 1.496 on 1 and 60 DF,  p-value: 0.2261
## 
## 
## [[12]]
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -51.09 -10.55  -0.76  11.15  44.31 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.1412     2.1884   0.065    0.949
## Predictor     0.3489     0.3108   1.123    0.266
## 
## Residual standard error: 17.28 on 61 degrees of freedom
## Multiple R-squared:  0.02024,    Adjusted R-squared:  0.004183 
## F-statistic:  1.26 on 1 and 61 DF,  p-value: 0.266
## [1] "There are 1 NA in the matrix X in Kelowna station"

## [1] "There are 1 NA in the matrix X in Kelowna station"

## [1] "There are 1 NA in the matrix X in Kelowna station"

## $Winter
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -70.023 -36.364  -5.873  29.224 111.165 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  263.636      5.783  45.588   <2e-16 ***
## Predictor     -1.361      0.595  -2.288   0.0256 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.2 on 61 degrees of freedom
## Multiple R-squared:  0.07902,    Adjusted R-squared:  0.06393 
## F-statistic: 5.234 on 1 and 61 DF,  p-value: 0.02563
## 
## 
## $Spring
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -79.948 -32.174  -0.383  33.268  87.311 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 250.4780     6.3037  39.735   <2e-16 ***
## Predictor     1.0201     0.4361   2.339   0.0226 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.13 on 61 degrees of freedom
## Multiple R-squared:  0.08232,    Adjusted R-squared:  0.06727 
## F-statistic: 5.472 on 1 and 61 DF,  p-value: 0.02262
## 
## 
## $Summer
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -84.20 -28.34   1.48  28.05 112.02 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 253.6918     5.7381  44.212   <2e-16 ***
## Predictor     0.7858     0.3530   2.226   0.0297 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.29 on 61 degrees of freedom
## Multiple R-squared:  0.07515,    Adjusted R-squared:  0.05999 
## F-statistic: 4.957 on 1 and 61 DF,  p-value: 0.02969
## 
## 
## $Fall
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -76.229 -33.578   4.456  31.865 113.254 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 255.8871     5.7324  44.639   <2e-16 ***
## Predictor     1.4104     0.9849   1.432    0.157    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43.26 on 61 degrees of freedom
## Multiple R-squared:  0.03252,    Adjusted R-squared:  0.01666 
## F-statistic: 2.051 on 1 and 61 DF,  p-value: 0.1572
## $Winter
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.644  -6.370  -1.502   7.853  44.049 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01724    2.28755  -0.008    0.994
## Predictor   -0.03184    0.23244  -0.137    0.892
## 
## Residual standard error: 16.36 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.000318,   Adjusted R-squared:  -0.01663 
## F-statistic: 0.01877 on 1 and 59 DF,  p-value: 0.8915
## 
## 
## $Spring
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.090  -7.713  -1.469   7.909  42.323 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   1.1801     2.4422   0.483    0.631
## Predictor    -0.1778     0.1728  -1.029    0.308
## 
## Residual standard error: 16.22 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.01763,    Adjusted R-squared:  0.0009797 
## F-statistic: 1.059 on 1 and 59 DF,  p-value: 0.3077
## 
## 
## $Summer
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.676  -7.304  -0.743   8.442  44.729 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.56134    2.24189  -0.250    0.803
## Predictor    0.07037    0.13652   0.515    0.608
## 
## Residual standard error: 16.32 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.004483,   Adjusted R-squared:  -0.01239 
## F-statistic: 0.2657 on 1 and 59 DF,  p-value: 0.6082
## 
## 
## $Fall
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -52.661  -8.028  -1.185   8.051  43.888 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.5305     2.1953  -0.242    0.810
## Predictor     0.2133     0.3720   0.573    0.568
## 
## Residual standard error: 16.31 on 59 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.005543,   Adjusted R-squared:  -0.01131 
## F-statistic: 0.3289 on 1 and 59 DF,  p-value: 0.5685
## $Winter
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.860  -8.733  -1.768  12.110  43.269 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.2931     2.3887   0.123    0.903
## Predictor    -0.1049     0.2458  -0.427    0.671
## 
## Residual standard error: 17.43 on 61 degrees of freedom
## Multiple R-squared:  0.002976,   Adjusted R-squared:  -0.01337 
## F-statistic: 0.1821 on 1 and 61 DF,  p-value: 0.6711
## 
## 
## $Spring
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.689 -10.185  -1.982  11.750  42.587 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.61273    2.60676   0.235    0.815
## Predictor   -0.09239    0.18034  -0.512    0.610
## 
## Residual standard error: 17.42 on 61 degrees of freedom
## Multiple R-squared:  0.004284,   Adjusted R-squared:  -0.01204 
## F-statistic: 0.2625 on 1 and 61 DF,  p-value: 0.6103
## 
## 
## $Summer
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.392 -10.019  -1.273  11.883  44.143 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.52160    2.36443  -0.221    0.826
## Predictor    0.06858    0.14544   0.472    0.639
## 
## Residual standard error: 17.43 on 61 degrees of freedom
## Multiple R-squared:  0.003632,   Adjusted R-squared:  -0.0127 
## F-statistic: 0.2224 on 1 and 61 DF,  p-value: 0.6389
## 
## 
## $Fall
## 
## Call:
## lm(formula = y ~ Predictor, data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -52.539 -11.274  -1.793  11.968  43.258 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.5690     2.3058  -0.247    0.806
## Predictor     0.2555     0.3962   0.645    0.521
## 
## Residual standard error: 17.4 on 61 degrees of freedom
## Multiple R-squared:  0.006771,   Adjusted R-squared:  -0.009512 
## F-statistic: 0.4158 on 1 and 61 DF,  p-value: 0.5214